Examining the Research Performance of Universities with Multi-Criteria Decision-Making Methods

Abstract

There has been an increasing interest in research performance in recent years. However, in previous studies, research performance has generally been measured by traditional methods. In this study, a model based on multi-criteria decision-making (MCDM) methods is proposed to evaluate the research performance of universities. In order to apply the proposed model, the research performance of 88 universities was analyzed with MCDM methods. The research performance of universities was examined with six criteria in total in the dimensions of productivity and impact with the last 5 years of data from InCites Benchmarking and Analytics. First, the criteria were weighted by CRITIC, MEREC, and Entropy methods. Each of these criteria weights was analyzed separately with ARAS, MABAC, and TOPSIS methods and a total of nine different rankings were obtained. The different ranking values were combined using the Borda function and the final ranking of each university was determined. According to the results of the study, the most important productivity criterion in performance evaluation is “number of documents in Q1 per academic staff” and the most important impact criterion is “h-index excluding self-citations per academic staff.” In this study, 88 universities were ranked according to their research performance with the proposed model. Validation and sensitivity findings showed that the proposed model is a robust, consistent and stable methodology for measuring research performance. With the methods proposed in this research, the research performance of researchers, countries and academic units as well as universities can be analyzed.